class_docstr: Groups context_encoder and cand_encoder together.
code: def forward(
      self,
      token_idx_ctxt,
      segment_idx_ctxt,
      mask_ctxt,
      token_idx_cands,
      segment_idx_cands,
      mask_cands,
  ):
      embedding_ctxt = None
      if token_idx_ctxt is not None:
          embedding_ctxt = self.context_encoder(
              token_idx_ctxt, segment_idx_ctxt, mask_ctxt
          )
      embedding_cands = None
      if token_idx_cands is not None:
          embedding_cands = self.cand_encoder(
              token_idx_cands, segment_idx_cands, mask_cands
          )
      return embedding_ctxt, embedding_cands
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.utils.torch import padded_3d
  from parlai.zoo.bert.build import download
  from parlai.utils.io import PathManager
  from .bert_dictionary import BertDictionaryAgent
  from .helpers import BertWrapper, BertModel, add_common_args, surround, MODEL_PATH
  import os
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the BiEncoderModule obj's underlying member method code with example usage: obj.forward(token_idx_ctxt, segment_idx_ctxt, mask_ctxt, token_idx_cands, segment_idx_cands, mask_cands) and returns: embedding_ctxt,  embedding_cands